In a cloud-based environment, users may receive vast amounts of data from a number of data sources such as content generators, databases, search engines, other users, and so on. For example, users may receive phone calls, email messages, calendar requests, text messages, and other types of data and alerts. Manually reading, responding, and organizing these vast amounts of data can be overwhelming, time-consuming, and inefficient for the individual users.
Some applications attempt to simplify user actions in response to the data by anticipating the actions the user may take upon receipt of the incoming data. Such applications may attempt to understand the behaviors of the user by classifying the user's behavior based on observed user response trends. However, many attempts have limitations as the observed trends are too simplistic, generic, broad, or vague to accurately predict how the user may respond to the incoming data.